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Nature Inspired Optimization for Spectrum Sensing and Allocation in Cognitive Radio Networks
Get PDFCognitive radio (CR) refers to intelligent radio technology that scans its environment to optimize spectrum use and adjusts its parameters accordingly. It employs a communication system that is aware of its surroundings, including spectrum usage and availability. A key aspect of CR is identifying idle channels by analyzing traffic patterns using effective learning strategies.
However, CRNs face challenges such as cross-layer design issues, spectrum sensing errors, hidden node problems, and complex spectrum management. Spectrum sensing is critical for accessing unused radio spectrum while minimizing interference. Efficient sensing techniques must be cost-effective, fast, and capable of detecting weak primary signals. Although researchers have proposed effective methods for spectrum utilization, significant complexities and errors persist.
Recent advancements aim to enhance spectrum sensing and allocation in CRNs. One approach involves the Enhanced Threshold-Based Energy Detection (ETBED) method, which uses a dynamic threshold based on signal ratio. The Modified Black Widow Optimization Algorithm (MBWO) further refines the threshold calculation, combining dynamic threshold detection with MBWO for improved performance. For spectrum allocation, the Enhanced Deep Reinforcement Learning Approach (EDRLA) integrates Deep Reinforcement Learning (DRL) and Adaptive xiii War Strategy (AWS).
This approach leverages historical channel utilization data to train models on channel and time correlations, with the Q-table updated using AWS. Additionally, the Modified Gannet Optimization Algorithm Entropy Detection (MGOAED) improves energy efficiency in CRNs. By incorporating the Oppositional Function (OF) into the Gannet Optimization Algorithm (GOA), the solution initialization process is enhanced, allowing CRNs to achieve better energyefficient operation
Design of Multi-objective Optimization Algorithms for VLSI Floor Planning
No full textVLSI floorplanning is a key design step that determines the optimal placement of circuit modules to minimize chip area, wire length, and heat generation. Existing swarm intelligence–based metaheuristics improve area and wire length but often ignore thermal effects.
To address this, the Multi-Objective Firefly Optimization–based Floorplanning (MOFO-FP) technique is introduced, using a Heat-Aware Firefly Optimization (HAFO) algorithm that minimizes heat, space, and wire length under fixed outline constraints. Each firefly represents a floorplan, with brightness indicating solution quality; dimmer fireflies move toward brighter ones to find optimal placements.
A second method, the Hybridized Multicriteria Ant Colony and Firefly Optimization (HMAC-FO), combines ACO and Firefly Optimization. ACO generates optimized initial populations for faster convergence, achieving reductions of 3.48% in area, 0.64% in wire length, and 3.33% in temperature on MCNC benchmarks.
The approach also handles placement constraints, both absolute (preplace, range, boundary) and relative (alignment, abutment, clustering), using horizontal and vertical constraint graphs for efficient constraint satisfaction.
Overall, the proposed hybrid bio-inspired multi-objective optimization algorithm achieves better chip performance by jointly minimizing area, wire length, and heat while meeting all placement constraints
Robustness of Aging Transition in Competing Effect of Heterogeneity and Symmetry Breaking Coupling
No full textThe knowledge of emergent dynamics and the mechanisms by which self-organizing complex systems operate is a perennially difficult problem that can be effectively resolved by employing an ensemble of coupled dynamical units as a framework. Several intriguing collective dynamical behaviors, such as synchronization, chimera, clustering, oscillation quenching, etc., have been reported using an ensemble of coupled oscillators. Failure or degradation in the performance of even one of the local oscillators in a network of oscillators can lead to cascading failures resulting in the collapse of the entire network such as power blackouts. Similar dynamics can also be observed among neuronal networks despite billions of neurons being born and dying daily.
For instance, neuronal pathological disorders such as Alzheimer’s disease are mainly due to the degradation/failure of neurons in assemblies. The report of Daido and Nakanishi has led to a flurry of research activities on the phenomenon of the aging transition. In the beginning stage, disorder-induced aging transition was reported in locally coupled oscillators, and the study was extended to a various coupling configurations. In particular time-delay effects on the aging transition in a large population coupled with nonlinear oscillators have also been reported. The aging transitions are analyzed so far only by adapting symmetry preserving global or locally coupled oscillators.
In this thesis, we investigate the macroscopic dynamics of networked oscillators, focusing on how the deterioration or failure of microscopic constituents affects the overall activity and stability of the heterogeneous network coupled via symmetry-breaking interaction. We begin by analyzing a globally coupled network of heterogeneous oscillators, deriving the evolution equation for two macroscopic order parameters. Then, we explore the dynamical robustness of the network by introducing limiting factors such as diffusion and self-feedback.
We deduce the evolution equation of two macroscopic order parameters from a globally coupled network of heterogeneous oscillators following the self-consistent field approach under the strong coupling limit. Further, we explore that the symmetry-breaking coupling facilitates the onset of the homogeneous steady state among the population at the critical proportion of the inactive oscillators despite large number of active oscillators. Interestingly, a chimera-like death state is observed in the study related to the aging transition for the first time in the literature.
The competing effect of heterogeneity and symmetry-breaking coupling on the emerging dynamics in a system of N globally coupled Stuart-Landau oscillators is investigated. Increasing the heterogeneity, Hopf bifurcation parameter’s standard deviation favors the macroscopic oscillatory state for low values of the symmetry-breaking coupling and the inhomogeneous steady state for larger coupling values. There is also a transition, tipping, to a homogeneous steady state (aging state) from the macroscopic oscillatory state.
Analytical stability (critical) curves of these bifurcations, deduced from the mean-field variables. From the result, we conclude that heterogeneity and symmetry-breaking coupling plays a crucial role in the aging phenomenon. Moreover, we find that the symmetry-breaking coupling can facilitate the onset of the aging transition, whereas the large heterogeneity factor facilitates the stable macroscopic oscillatory state by destabilizing the aging transition state. The aforementioned outcomes have given rise to numerous phenomena in neural networks and power grids
Development of novel algorithms for the investigation of small displacement optical flow for Opto Kinetic Nystagmus (OKN) detection and large displacement optical flow for surveillance
No full textMotion is a significant component of the modern visual experience, aiding with oculomotor control, object recognition, scene interpretation, perceptual organization and the acquisition of 3D shapes. Identifying motion vectors that characterize two-dimensional image transitions, usually from video frames, is necessary to estimate motion.
Optical flow is a motion-tracking technique that estimates the trajectory and velocity of a moving object in a video by shifting each pixel between frames using displacement vectors. This work has two objectives based on small and large displacement optical flow and computes optical flow using Lucas-Kanade and Brox flow, which seem to be superior to other methods.
A novel Subsampled Lucas-Kanade Optical Flow (SLKOF) algorithm has been proposed to detect Opto Kinetic Nystagmus (OKN) by analyzing the human eye\u27s small displacement. Adults experiencing limited visual perception may have neurological issues requiring OKN detection. Images are subsampled in the SLKOF, and the OKN gain is assessed over various subsampling factors.
The OKN experiment involves computer-controlled drum rotation via a stepper motor. The proposed method is compared with Lucas-Kanade (LK) optical flow based on OKN gain. The comparison illustrates that OKN gain of ¼ subsampling factor of SLKOF correlates with LK optical flow for 80% of cases. SLKOF algorithm for OKN detection with reduced image storage size and computation time has been developed.
The visual image quality of images captured by modern cameras is degraded by noise. By combining Contrast Limited Adaptive Histogram Equalization (CLAHE) and Denoising Convolutional Neural Network (DnCNN), the proposed CLAHE - DnCNN algorithm can denoise color images. The CLAHE - DnCNN algorithm is compared to Color Block Matching 3D-based adaptive TV denoising (CBM3D) and Color Denoising Convolutional Neural Networks -Blind (CDnCNN-B) methods using Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) metrics. On average, the proposed algorithm outperforms all test images.
The small size sensor in the Charge Coupled Device (CCD) camera reduces image resolution. Color Global Image Histogram Equalization (CGIHE) is integrated with Very Deep Super Resolution (VDSR) Network in the proposed CGIHE -VDSR algorithm to improve color image resolution. The PSNR and SSIM metrics reveal that the proposal surpasses the benchmark methods.
The Brox and SIFT flow methods analyze the traffic sequences with large displacements. However, noise makes the large displacement optical flow incurred by the various techniques unsuitable for surveillance. The Tetrolet, a Haar-based wavelet transform, is proposed to denoise the large displacement optical flow. The simulation results demonstrate that the proposed Tetrolet transform is better suited for SIFT flow than Brox flow in terms of PSNR
A Study on the Impact of Psychological Factors on Investment Decisions of Gen Y - IT Employees, Tamil Nadu
No full textInvestments are indeed vital to the economic development of a country. Individual savings and investment patterns play an important role in any economy since they are major components of financial markets. The investment pattern and type of investment products preferred by investors decide the future of the economy. As the Investment decision-making became crucial for individual investors since it can result in significant gains or losses and also has effect on the economy. As a result, each individual investor needs to be so cautious of how they make decisions and what influences them. The people were categorised by the generations as each of it has their own characteristics. The persons in each generation were defined in terms of different determinants. These discrepancies may be due to various factors like technological advancements, major events that took place, political differentiation, social differentiation, and economic differentiation (Baker, 2015).
Gen Y has fascinated researchers across the globe among several generational cohorts, which quite predictably makes them the most researched generation so far (Beaton, 2016). Subsequently, India’s IT industry has continued to have more employees, bringing the total to 5.4 million (5.7% year-over-year increase), solidifying its status as the ”Digital Talent Nation” for the entire world. In the past two decades, India has become a major global center for IT. Thus, the current study examines the investment decisions of Gen Y IT employees and the factors that influence their investment decisions. The investor’s decision-making may be influenced by various factors, including psychological and economic factors. Many significant factors involved in decisionmaking on the investment include personality, self-esteem, decision-making skills to
manage the funds, and the role of friends and family decisions (Sadiq & Khan, 2019). Since much of the basic theories of behavioural finance are concerned with personal, psychological and individual vulnerability against behavioural biases, risk attitude and time preference plays an essential role in investment decision-making. The study has taken psychological factors, behavioral biases and risk propensity to understand its impact on the investment decisions of Gen Y IT employees in TamilNadu. Under psychological factors the study has taken big five personality traits namely extroversion, openness, conscientiousness, agreeableness and neuroticism in addition to the other factor self-efficacy.
Subsequently under the behavioral bias three biases were taken for the study namely herd behaviour bias, overconfidence bias, and dispositional effect bias. And another factor that is used in the current study is risk propensity. The socio-economic factors used in the study are namely age, gender, marital status, location, income, savings and investment details. All these factors were taken to study its impact and relationship with the investment decision of Gen Y IT employees.
The data for the study is collected from the IT employees who are working in IT companies located in Chennai, Coimbatore, Madurai and Trichy of Tamilnadu as they are considered as the population of the study using a multistage stratified sampling method. From the total 800 questionnaires distributed across the four selected cities of Tamil Nadu, 671 have been collected with a response rate of 83.8%O˙ ut of these, 94 questionnaires were removed due to improper and partially filled responses. Finally, 577 questionnaire responses were taken for the study.
The study identifies that there exists a significant positive relationship between age, marital status, designation, annual income, annual gross savings and investment experience with the investment decisions of Gen Y IT employees. The correlation coefficient found to be higher for annual income, age and the investment decision, followed by marital status, annual gross savings and investment experience. The psychological factors namely, big five personality trait - extraversion trait has higher correlation with the investment decision followed by the openness to experience, agreeableness traits towards the investment decision of Gen Y IT employees. The psychological factor - self-efficacy also has a higher correlation with the investment decisions, which have a significant influence on the investment decisions of Gen Y IT Employees.
The R-squared value of 0.545 from the model states that a moderate accuracy (54.5%) in investment decisions is owing to herd behaviour bias. Compared to herd behavior bias, the model shows slightly higher prediction accuracy (60.3%) for overconfidence bias, with an R-squared of 0.603. The dispositional effect is a major (70.9%) influence on investing decisions, as seen by its stronger effect (R-squared = 0.709). Likewise, the R-squared value of 0.714 states that a high accuracy (71.4%) in investment decisions is owing to risk propensity.
Furthermore, the model shows a slightly higher prediction accuracy (74.5%) for behavioural bias, with an R-squared of 0.745. Based on the factors included in the model, it is inferred that the total model gets a R-squared value of 0.768, showing good predictive accuracy in describing investment decisions. Thus, the structural model shows strong predictive accuracy overall, accounting for (76.8%) of the influence in investment decisions. From the study, it is confirmed that there exists an influence of psychological factors, behavioural bias and risk propensity towards the investment decision of Gen Y IT employees in Tamil Nadu
Emergence Of Extreme Events In Nonlinear Oscillators Due To Parametric And External Excitations
No full textThis comprehensive PhD thesis immerses itself in the intricate world of dynamical systems, aiming to understand, characterize, and develop innovative mitigation strategies for rare and extreme events occurring within diverse nonlinear oscillatory models. The term events spans a broad array of occurrences within defined systems, with rare events standing out as infrequent outliers or deviations from the norm.
Rare events span various disciplines such as oceanography, climate studies, biology, economics, ecology, encompassing phenomena like rogue waves, floods, cyclones, earthquakes, and financial crises. Recognized for their potential to cause substantial harm to both society and the environment, these events are widely acknowledged as extreme events.
The journey into the dynamical study of extreme events originated with early research on rogue waves, utilizing the nonlinear Schrodinger equation as a foundational model. This research highlighted the significance of modulation instability in the production of rogue waves, emphasizing their rare occurrence and the resulting long-tailed statistical distribution of extreme events. Subsequent investigations extended to various domains, employing partial differential equations to model extreme occurrences in optics, hydrodynamics, chemical sciences, and finance.
Recent research delves into rare, recurrent, and large amplitude oscillations marked as extreme events within nonlinear dynamical systems, employing ordinary differential equations to simulate extreme events in physical, biological, and sociological systems. The primary objective is to unravel novel mechanisms and routes in nonlinear oscillators subjected to parametric excitation and external forcing. The overarching goal is to not only enhance our understanding but also pave the way for effective mitigation strategies.
The initial section of the thesis meticulously explores bursting dynamics in the Rayleigh-Lienard hybrid system, revealing periodic and chaotic bursting oscillations classified as extreme events. The concept of a pulse-shaped explosion is introduced, validated through the transformation of the system into a fast-slow system and utilizing a single slow variable. The study extends to a Mathews-Lakshmanan oscillator model, identifying distinct bifurcation routes leading to sudden, intermittent chaotic spikes. Comprehensive investigations are conducted not only to identify the development and mechanism of these occurrences but also to anticipate and proactively manage extreme events.
The second part of the thesis shifts its focus towards finding practical methods to mitigate extreme events. It delves into the dynamics of a periodically forced anharmonic oscillator and a forced Lienard oscillator with asymmetric potential wells. Linear damping emerges as a crucial mechanism for suppressing extreme events, providing valuable insights for control within the systems. The outcomes of these studies contribute significantly to advancing our comprehension of extreme events in diverse nonlinear oscillators, offering a robust foundation for guiding future research in this fascinating and complex field
Integrating GIS and CA-Markov Model to Study Urban Dynamics of Salem, India In Terms of Pattern, Intensity and Direction
No full textUrban populations have grown substantially in the modern era and the information age as people seek better living prospects. Currently, more than half of the world\u27s population resides in the cities. Such growth must be monitored to ensure that people enjoy the boons of urban spaces, not the banes. Recognising the significance of urban studies, several researchers have attempted to analyse and predict urban dynamics using remote sensing, Geographic Information Systems, and machine learning methods. However, certain limitations exist, especially in improving the CAMarkov model\u27s prediction performance. In addition, studies have suggested that growth characteristics, such as pattern, intensity, and direction, must be included as driving factors in prediction models. These hypotheses served as the basis of the present study. The research also emphasised the necessity for regional urban growth studies beyond its corporation limit.
Salem, a non-metropolitan city, was earmarked as a smart city because of its economic importance and was chosen as the region of interest. During the preliminary field visit, it was noticed that there was a considerable floating population from neighbouring towns, such as Omalur, Sankari, Rasipuram, and Vazhapadi. This prompted the conduct of a regional analysis that included these towns. Land Use/Land Cover (LULC) was the preliminary data required for this analysis. However, owing to the lack of a high-resolution LULC dataset, this study began by creating the LULC dataset using pan-sharpened Landsat images. Using Landsat data from 2020, the performances of three machine-learning algorithms the Random Forest Algorithm (RFA), Maximum Likelihood Classifier (MLC), and Support Vector Machine (SVM) were evaluated. The results showed that the SVM output had the highest accuracy of 0.945; subsequently, the LULC data were prepared for 2001 and 2011. The LULC classes include agricultural, built-up, others (barren, fallow land), and restricted (water bodies, mining, and forest). The created dataset yielded overall accuracies of 91.4%, 92.3%, and 95.2% for 2001, 2011, and 2020, respectively, with kappa statistics of 0.884, 0.896, and 0.935. These results demonstrate the reliability of the LULC maps for further research. Subsequently, changes in LULC were examined, revealing a consistent uptrend in urban expansion, characterised by the conversion of agricultural land to barren land and its subsequent development into built-up areas. The time-series analysis of changes in LULC revealed a steady increase in urban growth, characterized by converting agricultural land to barren land and eventually transforming it into builtup areas. This growth pattern is expected to continue and accelerate as the city expands and transforms into a smart city.
The subsequent phase involved comparing the prediction performance of the CA-Markov model using two methods: 1) without driver variables and 2) with driver variables. The model output without drivers exhibited a kappa statistic of approximately 0.75. Although the value was statistically significant, differences in the area measures were noted. The predicted built-up area exceeded the actual value by 111 square kilometres. These results indicate the need to incorporate drivers. Subsequently, the study measures urban growth characteristics, such as direction, pattern, and intensity, and incorporates them as drivers in a limited data scenario. The objectives of the directional study were to (1) delineate the limits of urban growth for Salem and its neighbouring towns, (2) conduct directional analysis within these limits, (3) use centroid shift analysis to explore the macro-level direction of urban growth using centroid shift analysis, and (4) use landscape metrics to quantify the dynamics of urban cover changes in Salem and neighbouring towns. The major outcomes of the study are that the study provided evidence of interconnectedness between the city and towns and the that the region is still in urbanization phase and has potential for planned development. In addition, the analysis of the built-up extent across the three study periods revealed that, contrary to the current corporation de jure boundary of 9 km, the actual extent of the city was 12 km from the city centre. This emphasises the importance of upgrading city boundaries to enable proactive urban planning.
The study then mapped four different types of growth patterns for 2001 2011 and 2011 2020: infill, sprawl, scattered, and ribbon. The study revealed a significant increase in built-up areas between 2011 and 2020, with ribbon development emerging as the most common type of urban growth. According to the findings, the main drivers of urbanisation are population increase and economic development. Additionally, the findings demonstrate that urban growth is taking place rapidly on the city\u27s periphery, encroaching on agricultural land and potentially impacting the local economy. The study also found that neighbouring towns, namely Omalur, Rasipuram, Sankari, and Vazhapadi, influence the urban growth patterns of Salem. These changes are attributed to the dynamic interaction between population growth, accessibility, agricultural land, and urban planning considerations.
The Urban Expansion Differentiation Index (UEDI) was used to identify the dominant pattern for each growth. The studies found that sprawl is dominant pattern, particularly on the fringes of cities, resulting in encroachment of agricultural land, particularly around Salem. This trend could negatively influence agricultural productivity and the local economy. The Urban Expansion Intensity Index (UEII) was then used to determine the intensity of growth, which was classified into five categories: very high, high, medium, low, and very low. Between 2001 and 2011, high-and very high-intensity values were primarily found in the core areas of Salem and Omalur.
However, these high-intensity values exhibited a dispersal pattern during the subsequent decade (2011-2020), becoming more common in the city and town cores and suburban areas. The objective of this study is to improve the model by incorporating these patterns. Consequently, a future growth region was created around pre-existing built-up areas by considering both pattern and intensity. A novel method was developed and a region was generated using this new algorithm within an arcpy environment. In the final phase, the CA Markov model was executed using the ultimate drivers selected using the Cramer\u27s V index. The drivers include pattern, intensity, and direction based on growth characteristics. The model performed well, with a kappa value of 0.9 and a 9.01% difference between the actual and predicted built-up areas. Urban growth was then predicted for 2030 2100 at 10-year intervals. The predictions indicate that growth will be at its highest over the next three decades before experiencing a notable decline after 2070.
Additionally, a prediction was made based on a simulated scenario to comprehend unidirectional growth by hypothetically limiting the growth to Salem. The output was compared with the bidirectional Business as Usual (BAU) scenario. Unidirectional analyses suggest a potential merger between Salem and Omalur by 2100, whereas bidirectional outcomes anticipate conurbation by 2050. These findings demonstrate the importance of bidirectional analysis, which accounts for growth disparities, in offering a comprehensive perspective on regional studies. In summary, this study provides insight into the urban dynamics of Salem and its peripherals, and presents a novel approach to model future growth using the CA-Markov model. Thus, blazing a trail for forthcoming researchers in the field of urban studies
Mg3Sb2-based Materials for Mid-Temperature Thermoelectric Application
No full textRecently, there has been growing research in the scientific community regarding the conversion of heat flux into electricity, which has proven effective in mitigating energy crises. Solid-state thermoelectric (TE) devices use the Seebeck effect to efficiently convert waste heat into electricity. Traditional materials contain toxic, rare, and expensive elements like Lead, Tellurium, and Germanium that restrict large-scale production, commercialization, and practical utilization.
Recently, Mg3Sb2-based materials have been attractive due to their remarkable characteristics, including abundance, affordability, and environmental friendliness. Furthermore,n-type Mg3Sb2-based materials exhibit high TE performance because of their high band degeneracy in the conduction band minimum (CBM), and low lattice thermal conductivity contributes to the enhanced TE performance of n-type Mg3Sb2-based materials. However, p-type Mg3Sb2-based material has a lower TE performance, highlighting the importance of improving it.
Herein, we focus on the p-type Mg3Sb2-based material mid-temperature thermoelectric application. Mg3Sb2- based materials have been synthesized through the solid-state reaction by evacuation-and-encapsulation technique. The thermoelectric transport behaviour of the samples has been investigated and optimized the Figure of merit (zT) of the p-type Mg3Sb2-based materials such as (i) Sn doped p-type Mg3Sb2 material, (ii) p-type Mg3-xZnxSb2/Sb composites: the role of ZnSb/Sb composite, and (iii) Zn and Ag co-doped p-type Mg3Sb2.
Among them, the Mg2Zn1-yAgySb2 (y = 0.03) sample has the highest zT of 0.95 at 725 K. Ga-doped n-type Mg3Sb2 was synthesized and studied its structural and thermoelectric transport behaviour. A prototype thermoelectric generator has been fabricated with the 8-pairs using p-leg Mg2Zn0.97Ag0.03Sb2 and n-leg Mg3.24Ga0.06Sb1.48Bi0.48Te0.04 materials. The maximum efficiency of 5.68% at the TH = 700 K was achieved for the fabricated device
Experimental Investigation on Downstream Characteristics of S809 Airfoil With and Without Passive Flow Control Device
No full textThe wake behaviour of extended flat plate (EFP) and serration in the trailing edge of S809 airfoil is presented in this experimental study using wind tunnel testing for several freestream turbulence intensities (TI). The clustering wind turbines in wind parks has recently been an important problem, as it involves determining the number of wind turbines to be installed to increase the power output.
The downstream wake characteristics are one of the most significantparameter that influence the performance of subsequent wind turbines.These wind turbine blades are made up of airfoil cross sections and need to be studied in detail. A series of wind tunnel experiments have been conducted to assess the downstream near-wake characteristics of the S809 airfoil at different angles of attack corresponding to the Reynolds number (Re) = 2.11 × 105.
These experimental results discovered the complex flow behaviors of the downstream near-wake characteristics, featuring significant irregularity arising from the chaotic flow separations dominating over the suction sides of the airfoil.This phenomenon influenced to increasing velocity deficit at post stall region. Based on the experimental results, it is found that the velocity deficit increase from TI 10% to 12% makes the optimal TI as 10% only.
Nonetheless, as the extended trailing-edge amplitude (A) increases, all the downstream velocity profiles broaden significantly, and the non-dimensional vortex shedding frequencies decrease. The base model with serration model wave length (λ) = 0.2C, A = 0.1C shows significant changes in wake intensity downstream. Furthermore, experiments were conducted to better understand the wake characteristics at various downstream locations such as 1C, 2C, 3C, and 4C axial chord
Development of Anti-Fibrotic Agents For Potential Use In Glaucoma Filtration Surgery to Prevent Scar Formation
No full textGlaucoma is the second leading cause of blindness with irreversible vision loss. It is marked by the degeneration of retinal ganglion cells, which results in optic nerve head damage. Intraocular pressure (IOP) is a prime risk factor of glaucoma controlled by various treatment regimens i.e., medication, laser, and/ or surgery. It is reported that longterm exposure to anti-glaucoma medications (AGM) may subtle the surgical success rate leading to postoperative fibrosis. Transforming growth factor beta (TGF-β) is a growth factor that plays an important role in the development of glaucoma and promotes fibrosis.
This study is aimed at developing peptides to inhibit TGF-β as a therapeutic strategy. The total protein concentration in the aqueous humour (AH) was found to be substantially higher (p \u3c 0.05) in primary angle closure glaucoma (PACG) and primary open angle glaucoma (POAG) patients compared to controls in the current study. Interestingly, mass spectrometry analysis unraveled numerous extracellular matrix
(ECM) proteins in PACG compared to POAG. Further, this study reports significantly increased expression of growth factors and ECM molecules (p \u3c 0.05), both at protein and transcript levels in AH and tenon\u27s tissue in glaucoma filtration surgery (GFS) patients compared to the cataract controls. While remarkably lowered expression of decorin, an anti-fibrotic molecule in AH and tenons of POAG and PACG compared to the control is a striking finding.
This study had shown a significant increase (p \u3c 0.05) in the specific protein of AH i.e., CTGF, TGF-β1, TGF-β2, ELN, SPARC, LOXL2, and a decrease (p \u3c 0.05) in DCN in primary glaucoma patients (i.e., POAG and PACG) with increased duration of preoperative AGMs (\u3e 10 yrs), cup-to-disc ratio (CDR) \u3c 0.5, visual field index (VFI) below 25 %, preoperative IOP \u3e 20 mm of Hg and postoperative bleb with poor surgical
outcome. This study demonstrated a notable ECM alteration with increased SPARC, LOXL2, collagen levels, and a significant decrease in DCN, in human tenon’s fibroblast (HTFs) on exposure to various AGMs ± TGF-β1 co-treated conditions.
This study also revealed that peptides from the leucine rich repeat 5 (LRR5) of small leucine rich proteoglycans (SLRP) derived at 100 μM concentration significantly (p \u3c 0.05) inhibited HTFs migration, downregulated TGF-β1 mediated phosphorylated Smad signaling, decreased collagen synthesis, inhibited collagen gel contraction, and decreased ECM remodeling proteins in TGF-β1 co-treated condition. Collectively, this study is the first report to show increased ECM remodeling proteins and lowered expression of DCN in AH and tenon’s tissue of primary glaucoma patients which is a prominent finding. The present study has provided novel insights on the anti-fibrotic role of SLRP derived peptides in HTFs; thereby it may minimize postoperative fibrosis to improve GFS outcomes which warrant further investigation